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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Intermediate Business analysts and developers collecting, curating, analyzing, and reporting on crucial business data with basic statistics and R or other scripting language skills

Who should attend & recommended skills

  • Intermediate Business analysts and developers who are increasingly collecting, curating, analyzing, and reporting on crucial business data. This course is geared toward those who want to learn R language and its associated tools to provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.
  • Skill-level: Foundation-level Practical Data Science skills for Intermediate skilled team members. This is not a basic class.
  • Basic statistics: Basic (1-2 years’ experience)
  • R or another scripting language: Basic (1-2 years’ experience)
  • Data science background not needed

About this course

Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.

Skills acquired & topics covered

  • Basic principles without the theoretical mumbo-jumbo, jumping right to the real use cases you’ll face
  • Collecting, curating, and analyzing the data crucial to the success of your business.
  • Applying the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
  • Data science for the business professional
  • Statistical analysis using the R language
  • Project lifecycle, from planning to delivery
  • Numerous instantly familiar use cases
  • Keys to effective data presentations

Course breakdown / modules

  • The roles in a data science project
  • Stages of a data science project
  • Setting expectations

  • Working with data from files
  • Working with relational databases

  • Using summary statistics to spot problems
  • Spotting problems using graphics and visualization

  • Cleaning data
  • Sampling for modeling and validation

  • Mapping problems to machine learning tasks
  • Evaluating models
  • Validating models

  • KDD and KDD Cup 2009
  • Building single-variable models
  • Building models using many variables

  • Using linear regression
  • Using logistic regression

  • Cluster analysis
  • Association rules

  • Using bagging and random forests to reduce training variance
  • Using generalized additive models (GAMs) to learn non-monotone relationships
  • Using kernel methods to increase data separation
  • Using SVMs to model complicated decision boundaries

  • The buzz dataset
  • Using knitr to produce milestone documentation
  • Using comments and version control for running documentation
  • Deploying models

  • Presenting your results to the project sponsor
  • Presenting your model to end users
  • Presenting your work to other data scientists